# SnapCache
<a href="https://pypi.python.org/pypi/snapcache"><img src="http://img.shields.io/pypi/v/snapcache.svg" alt="Latest version on PyPI"></a> <a href="https://pypi.python.org/pypi/snapcache"><img src="https://img.shields.io/pypi/pyversions/snapcache.svg" alt="Compatible Python versions."></a>
Snap-in memory caching for Python, no persistence, just pure function result caching.
## Motivation
Python’s built-in `functools.lru_cache` offers an easy way to cache function results, but it has limitations. For example, attempting to cache complex objects like `NumPy` arrays results in a `TypeError: unhashable type: 'numpy.ndarray'`.
**SnapCache** addresses this issue by offering a similar simple decorator interface while supporting caching for any data structure ✨.
## Installation
To install **SnapCache**, use `pip`:
```bash
pip install snapcache
```
## Usage
### Function-level Caching
You can cache function results using the `WithCache` decorator. Just apply it to any function you want to memoize.
```python
from snapcache import WithCache
@WithCache(maxsize=3)
def add(a, b):
return a + b
# Call the function multiple times
print(add(1, 2)) # First call, result is computed and cached
print(add(1, 2)) # Second call, result is retrieved from cache
```
### Method-level Caching
You can also apply the decorator to class methods:
```python
class Foo:
@WithCache(maxsize=3)
def bar(self, a, b):
return a - b
foo = Foo()
print(foo.bar(10, 5)) # First call, result is computed and cached
print(foo.bar(10, 5)) # Second call, result is retrieved from cache
```
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.
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"description": "# SnapCache\n\n<a href=\"https://pypi.python.org/pypi/snapcache\"><img src=\"http://img.shields.io/pypi/v/snapcache.svg\" alt=\"Latest version on PyPI\"></a> <a href=\"https://pypi.python.org/pypi/snapcache\"><img src=\"https://img.shields.io/pypi/pyversions/snapcache.svg\" alt=\"Compatible Python versions.\"></a>\n\nSnap-in memory caching for Python, no persistence, just pure function result caching.\n\n## Motivation\n\nPython\u2019s built-in `functools.lru_cache` offers an easy way to cache function results, but it has limitations. For example, attempting to cache complex objects like `NumPy` arrays results in a `TypeError: unhashable type: 'numpy.ndarray'`.\n\n**SnapCache** addresses this issue by offering a similar simple decorator interface while supporting caching for any data structure \u2728.\n\n## Installation\n\nTo install **SnapCache**, use `pip`:\n\n```bash\npip install snapcache\n```\n\n## Usage\n\n### Function-level Caching\n\nYou can cache function results using the `WithCache` decorator. Just apply it to any function you want to memoize.\n\n```python\nfrom snapcache import WithCache\n\n@WithCache(maxsize=3)\ndef add(a, b):\n return a + b\n\n# Call the function multiple times\nprint(add(1, 2)) # First call, result is computed and cached\nprint(add(1, 2)) # Second call, result is retrieved from cache\n```\n\n### Method-level Caching\n\nYou can also apply the decorator to class methods:\n\n```python\nclass Foo:\n @WithCache(maxsize=3)\n def bar(self, a, b):\n return a - b\n\nfoo = Foo()\nprint(foo.bar(10, 5)) # First call, result is computed and cached\nprint(foo.bar(10, 5)) # Second call, result is retrieved from cache\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.\n",
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